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Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

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  • Luis Hernandez

    (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Autovía de Navarra A15, salida 56, Lubia 42290, Soria, Spain)

  • Carlos Baladrón

    (Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Javier M. Aguiar

    (Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Belén Carro

    (Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Antonio J. Sanchez-Esguevillas

    (Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Jaime Lloret

    (Universidad Politécnica de Valencia, Departamento de Comunicaciones, Camino Vera s/n. 46022, Valencia, Spain)

Abstract

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc. ), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network ( ANN ) that performs Short-Term Load Forecasting ( STLF ). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid , as microgrids appear to be the future of electric power supply.

Suggested Citation

  • Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:3:p:1385-1408:d:24008
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    References listed on IDEAS

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